Mobile electronic devices such as media players, mobile phones, tablet-based devices, etc., are rapidly becoming ubiquitous throughout most of the world. While a few studies have examined the proximity of people to their electronic devices, such as mobile phones, and where people have those devices while out and about, there is relatively little data about how people actually carry or position such devices. For example, while it is intuitive to assume that people carry, transport, or temporarily place their devices in pockets, bags, and purses, set them on tables or counters, hold them in their hands, etc., there is little in the way of specific facts with respect to where people keep their electronic devices at any particular point in time. Further, factors that may influence where a person puts her device at any particular time have not generally been well investigated.
Various recent studies have focused on specific interview questions, such as how often a person's phone is in their immediate proximity. Once such study found broad variation across participants with individual proximity levels for the phone being within arm's reach, ranging from 17% to 85%. Whether or not the person was home, sleeping, or if it was the weekend had the biggest impact on behavior. Another similar study determined that user's phones were within arm's reach 53% of the time and within the same room as the user 88% of the time. This study also highlighted that a user's context and routine affected phone proximity, for example more likely leaving the phone out when at home and more likely carrying it in a pocket or purse outside of the home.
Another recent study conducted a large-scale interview study asking large numbers of people where they keep their phones when they are out, why they chose this place, and if it was the usual place. The results of this study were strongly divided by gender, with 57% of men reporting that their phone was in their trouser pocket (8% for women), and 76% of women reporting that their phones were kept in a shoulder bag or backpack (10% for men). They also reported reasons that participants identified for placing the phone outside of the normal location, including variations in clothing (e.g., no pockets), expecting a phone call, or not wanting to be interrupted.
In addition to the types of studies noted above, various techniques have been suggested for using accelerometer and gyroscopic data for detecting whether a phone is being held in a user's hand or is in a user's pocket or purse in the case that a user is actively walking. This information is then used to implement various solutions for solution for preventing unintended phone operation. Unfortunately, such techniques are unable to classify any off-body locations (e.g., in a bag or on a desk).
Other existing approaches aim to be activity invariant by incorporating data from other sensors either in addition to or instead of accelerometers. For example, one such approach used a light sensor in a lab environment to determine when a phone was in a trouser pocket or out of the pocket. Another approach used features derived from listening on the microphone and a multi-round classification approach to infer whether the phone was in a user's pocket or not.
Another technique for evaluating device sensors uses a combination of gyroscope, accelerometer, compass, light, and proximity sensors to infer when the user picks up the phone to receive a call so that the ringtone volume could be muted when the phone is picked up. A somewhat related technique uses capacitive sensors on each side of a phone-shaped prototype to determine how a user is holding the device, with that information then being used to classify a set of different hand-grip configurations.
In contrast to the techniques noted above, various active approaches for providing or causing observable results have been suggested. For example, one such technique activates a vibration motor on the device and detects movement using an accelerometer. This active vibration information is evaluated in combination the emission of short ‘beeps’ while listening on the microphone and accelerometer to fingerprint an environmental response. A potentially less noticeable active approach uses a photo resistor and a light-to-frequency converter for sensing light from set of colored LEDs. Such approaches are used to determine various device placement scenarios.